A process may be described as a series of nodes or steps (e.g., actions, changes, or functions) that bring about a result. Processes may be used to define a wide range of activities such as the steps in a computer program, procedures for combining ingredients, manufacturing of an apparatus, and so forth. Further, metrics or process measurements may be defined to allow for process monitoring and data retrieval.
Specifically, metrics may be defined as properties of a process or business that are pertinent or that a user finds interesting. For example, business metrics may reflect business goals and include such things as cost, quality, outcome, and/or duration. Additionally, service level agreements (SLAs) inherently have underlying metrics. For example, a duration metric underlies a SLA requiring delivery of items no more than twenty-four hours after an order is placed. The “no more than twenty-four hours” requirement is merely a condition on a duration metric. Further, values for metrics may be computed using process execution data.
Process execution data may be defined as information or data related to a process instance. Executions or execution results in a process instance may be recorded using monitoring equipment, thus creating process execution data. Examples of process execution data include time stamps, orders, starting time, and ending time. A process definition may be composed of nodes (steps in the process), and arcs (connectors that define an order of execution among the nodes). During a process instance (i.e., an execution of a process definition), a certain node or string of nodes in the process may be executed zero, one, or many times. Accordingly, when a process instance is active (i.e., during execution), the availability of node execution data from that particular instance may be limited. This limited data may be referred to as partial process execution data. Further, the number of node executions (e.g., zero, one, or many) may depend on a process definition or formal description of a business process.
Existing tools, systems, and techniques may allow for the defining and computing of business metrics on top of business process execution data. For example, a tool may allow a user to define metrics, which may then be used to provide reports and/or monitoring of execution data associated with the metrics. Additionally, methods and systems may exist for deriving explanations and predictions regarding such metrics. These techniques may contemplate computing prediction models using process execution data acquired from active process instances (i.e., partial process execution data). For example, a tool may contemplate using a data mining technique to provide, at the very start of a process instance, a prediction for the value of one or more metrics. Further, the tool may provide an updated prediction as the execution proceeds based on the more current execution data. While existing techniques may be useful, a method to address the problem of computing a point or stage in a process execution where it makes sense to collect data and generate a prediction may provide a desirable additional benefit. The present disclosure may address the above issues and provide other advantages.